PROJECT SUMMARY DESCRIPTION: Social deficits, central to the diagnosis of autism spectrum disorders (ASDs), persist over time, influence functioning, and are predictive of long-term outcome. However, a range of social impairment exists among those affected. To date, brain alterations underlying impaired social cognition are not consistently shown across studies comparing a group of individuals with ASDs, to a group of healthy controls. Similarly, people with schizophrenia spectrum disorders (SSDs) demonstrate a range of social cognitive impairments and associated brain alterations have not been consistent. The well-established biological and clinical heterogeneity of these disorders has, thus far, been a major obstacle to the identification of neural circuit biomarkers of social cognitive impairment. The Research Domain Criteria (RDoC) approach can capitalize on clinical and biological heterogeneity to identify novel brain-behavior relationships that may cut across SSD or ASD diagnoses. Our pilot data show that structural/functional features of key social cognitive circuits are associated with social cognitive performance along a continuum in healthy controls (HCs), ASDs, and SSDs (and across a broader range of variation than in HCs and SSDs alone). In the proposed study, we have a unique opportunity ? to recruit a matched sample of individuals with ASDs using identical neuroimaging and behavioral assessments as implemented at the lead site of an already funded study in HCs and people with SSDs, within the RDoC Social Processes Domain (1/3R01MH102324). The first aim of our study is to identify dimensional relationships, among brain circuit structure, brain circuit function, and social cognitive performance across HCs, participants with SSDs and ASDs. The inclusion of participants with ASDs will enhance variation in social cognitive performance across our sample and allow us to detect the full range of brain-behavior relationships that underpin social cognition across HCs, ASDs, and SSDs. Further, we hypothesize that some individuals with SSDs may be more similarly impaired at the neural circuit and behavioral level to some individuals with ASDs, as opposed to those within their own diagnostic group. Therefore, we will also apply a novel data-driven approach, known as Similarity Network Fusion (SNF), to identify subgroups of individuals with similar profiles of neural circuit and social cognitive impairment that cut across conventional diagnoses. The ultimate aim of the current proposal is to identify brain-behavior relationships in the Social Processes Domain that cut across DSM diagnoses, and also parse subsets of people with distinct social cognitive brain-behavior profiles. If successful, when taken together, these two aims will accelerate mechanism-driven treatment development relevant across ASDs and SSDs that can improve social function.